scikit-learn:0. user_guide——需要学习的所有内容
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内容来自:http://scikit-learn.org/stable/index.html
- 1. Supervised learning
- 1.1. Generalized Linear Models
- 1.2. Linear and quadratic discriminant analysis
- 1.3. Kernel ridge regression
- 1.4. Support Vector Machines
- 1.5. Stochastic Gradient Descent
- 1.6. Nearest Neighbors
- 1.7. Gaussian Processes
- 1.8. Cross decomposition
- 1.9. Naive Bayes
- 1.10. Decision Trees
- 1.11. Ensemble methods
- 1.12. Multiclass and multilabel algorithms
- 1.13. Feature selection
- 1.14. Semi-Supervised
- 1.15. Isotonic regression
- 1.16. Probability calibration
- 2. Unsupervised learning
- 2.1. Gaussian mixture models
- 2.2. Manifold learning
- 2.3. Clustering
- 2.4. Biclustering
- 2.5. Decomposing signals in components (matrix factorization problems)
- 2.6. Covariance estimation
- 2.7. Novelty and Outlier Detection
- 2.8. Density Estimation
- 2.9. Neural network models (unsupervised)
- 3. Model selection and evaluation
- 3.1. Cross-validation: evaluating estimator performance
- 3.2. Grid Search: Searching for estimator parameters
- 3.3. Model evaluation: quantifying the quality of predictions
- 3.4. Model persistence
- 3.5. Validation curves: plotting scores to evaluate models
- 4. Dataset transformations
- 4.1. Pipeline and FeatureUnion: combining estimators
- 4.2. Feature extraction
- 4.3. Preprocessing data
- 4.4. Unsupervised dimensionality reduction
- 4.5. Random Projection
- 4.6. Kernel Approximation
- 4.7. Pairwise metrics, Affinities and Kernels
- 4.8. Transforming the prediction target (y)
- 5. Dataset loading utilities
- 5.1. General dataset API
- 5.2. Toy datasets
- 5.3. Sample images
- 5.4. Sample generators
- 5.5. Datasets in svmlight / libsvm format
- 5.6. The Olivetti faces dataset
- 5.7. The 20 newsgroups text dataset
- 5.8. Downloading datasets from the mldata.org repository
- 5.9. The Labeled Faces in the Wild face recognition dataset
- 5.10. Forest covertypes
- 6. Strategies to scale computationally: bigger data
- 6.1. Scaling with instances using out-of-core learning
- 7. Computational Performance
- 7.1. Prediction Latency
- 7.2. Prediction Throughput
- 7.3. Tips and Tricks
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- scikit-learn:0. user_guide——需要学习的所有内容
- scikit-learn:0. user_guide——需要学习的所有内容
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